Ensemble of Binary Classifiers Combined Using Recurrent Correlation Associative Memories
This is an incremental improvement for binary classification tasks, offering a novel combination scheme for ensemble methods.
The paper tackled the problem of improving classifier ensembles for binary classification by proposing a method that uses recurrent correlation associative memories (RCAMs) to combine base classifiers, showing it can be viewed as a weighted majority vote and confirming its potential through computational experiments.
An ensemble method should cleverly combine a group of base classifiers to yield an improved classifier. The majority vote is an example of a methodology used to combine classifiers in an ensemble method. In this paper, we propose to combine classifiers using an associative memory model. Precisely, we introduce ensemble methods based on recurrent correlation associative memories (RCAMs) for binary classification problems. We show that an RCAM-based ensemble classifier can be viewed as a majority vote classifier whose weights depend on the similarity between the base classifiers and the resulting ensemble method. More precisely, the RCAM-based ensemble combines the classifiers using a recurrent consult and vote scheme. Furthermore, computational experiments confirm the potential application of the RCAM-based ensemble method for binary classification problems.